In this project we study the use of neural networks as a tool for particle track pattern recognition with the possibility of its implementation in the Trigger system at the ATLAS experiment [1]. By using a method named Hough transform we created a Convolutional Neural Network (CNN) that is able to train on the transformed images of muons merged with minimum bias. We give an overview of how the CNN works and compare the results from the CNN with the old cut based method. We believe to have managed to find an alternative to the previously used algorithm, that is faster and more efficient.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-454374 |
| Date | January 2021 |
| Creators | Cardoso, Mário |
| Publisher | Uppsala universitet, Högenergifysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | FYSAST ; FYSMAS1167 |
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